Multimodal stimulation screens reveal unique and shared genes limiting T cell fitness.

Genes limiting T cell antitumor activity may serve as therapeutic targets. It has not been systematically studied whether there are regulators that uniquely or broadly contribute to T cell fitness. We perform genome-scale CRISPR-Cas9 knockout screens in primary CD8 T cells to uncover genes negatively impacting fitness upon three modes of stimulation: (1) intense, triggering activation-induced cell death (AICD); (2) acute, triggering expansion; (3) chronic, causing dysfunction. Besides established regulators, we uncover genes controlling T cell fitness either specifically or commonly upon differential stimulation. Dap5 ablation, ranking highly in all three screens, increases translation while enhancing tumor killing. Loss of Icam1-mediated homotypic T cell clustering amplifies cell expansion and effector functions after both acute and intense stimulation. Lastly, Ctbp1 inactivation induces functional T cell persistence exclusively upon chronic stimulation. Our results functionally annotate fitness regulators based on their unique or shared contribution to traits limiting T cell antitumor activity.

CRISPRi screen for enhancing heterologous α-amylase yield in Bacillus subtilis.

Yield improvements in cell factories can potentially be obtained by fine-tuning the regulatory mechanisms for gene candidates. In pursuit of such candidates, we performed RNA-sequencing of two α-amylase producing Bacillus strains and predict hundreds of putative novel non-coding transcribed regions. Surprisingly, we found among hundreds of non-coding and structured RNA candidates that non-coding genomic regions are proportionally undergoing the highest changes in expression during fermentation. Since these classes of RNA are also understudied, we targeted the corresponding genomic regions with CRIPSRi knockdown to test for any potential impact on the yield. From differentially expression analysis, we selected 53 non-coding candidates. Although CRISPRi knockdowns target both the sense and the antisense strand, the CRISPRi experiment cannot link causes for yield changes to the sense or antisense disruption. Nevertheless, we observed on several instances with strong changes in enzyme yield. The knockdown targeting the genomic region for a putative antisense RNA of the 3' UTR of the skfA-skfH operon led to a 21% increase in yield. In contrast, the knockdown targeting the genomic regions of putative antisense RNAs of the cytochrome c oxidase subunit 1 (ctaD), the sigma factor sigH, and the uncharacterized gene yhfT decreased yields by 31 to 43%.

Alternative cleavage and polyadenylation generates downstream uncapped RNA isoforms with translation potential.

The use of alternative promoters, splicing, and cleavage and polyadenylation (APA) generates mRNA isoforms that expand the diversity and complexity of the transcriptome. Here, we uncovered thousands of previously undescribed 5' uncapped and polyadenylated transcripts (5' UPTs). We show that these transcripts resist exonucleases due to a highly structured RNA and N6-methyladenosine modification at their 5' termini. 5' UPTs appear downstream of APA sites within their host genes and are induced upon APA activation. Strong enrichment in polysomal RNA fractions indicates 5' UPT translational potential. Indeed, APA promotes downstream translation initiation, non-canonical protein output, and consistent changes to peptide presentation at the cell surface. Lastly, we demonstrate the biological importance of 5' UPTs using Bcl2, a prominent anti-apoptotic gene whose entire coding sequence is a 5' UPT generated from 5' UTR-embedded APA sites. Thus, APA is not only accountable for terminating transcripts, but also for generating downstream uncapped RNAs with translation potential and biological impact.

Ribosome impairment regulates intestinal stem cell identity via ZAKɑ activation.

The small intestine is a rapidly proliferating organ that is maintained by a small population of Lgr5-expressing intestinal stem cells (ISCs). However, several Lgr5-negative ISC populations have been identified, and this remarkable plasticity allows the intestine to rapidly respond to both the local environment and to damage. However, the mediators of such plasticity are still largely unknown. Using intestinal organoids and mouse models, we show that upon ribosome impairment (driven by Rptor deletion, amino acid starvation, or low dose cyclohexamide treatment) ISCs gain an Lgr5-negative, fetal-like identity. This is accompanied by a rewiring of metabolism. Our findings suggest that the ribosome can act as a sensor of nutrient availability, allowing ISCs to respond to the local nutrient environment. Mechanistically, we show that this phenotype requires the activation of ZAKɑ, which in turn activates YAP, via SRC. Together, our data reveals a central role for ribosome dynamics in intestinal stem cells, and identify the activation of ZAKɑ as a critical mediator of stem cell identity.

Identifying ribosome heterogeneity using ribosome profiling.

Recent studies have revealed multiple mechanisms that can lead to heterogeneity in ribosomal composition. This heterogeneity can lead to preferential translation of specific panels of mRNAs, and is defined in large part by the ribosomal protein (RP) content, amongst other things. However, it is currently unknown to what extent ribosomal composition is heterogeneous across tissues, which is compounded by a lack of tools available to study it. Here we present dripARF, a method for detecting differential RP incorporation into the ribosome using Ribosome Profiling (Ribo-seq) data. We combine the 'waste' rRNA fragment data generated in Ribo-seq with the known 3D structure of the human ribosome to predict differences in the composition of ribosomes in the material being studied. We have validated this approach using publicly available data, and have revealed a potential role for eS25/RPS25 in development. Our results indicate that ribosome heterogeneity can be detected in Ribo-seq data, providing a new method to study this phenomenon. Furthermore, with dripARF, previously published Ribo-seq data provides a wealth of new information, allowing the identification of RPs of interest in many disease and normal contexts. dripARF is available as part of the ARF R package and can be accessed through https://github.com/fallerlab/ARF.

CRISPR/Cas9 gRNA activity depends on free energy changes and on the target PAM context.

A major challenge of CRISPR/Cas9-mediated genome engineering is that not all guide RNAs (gRNAs) cleave the DNA efficiently. Although the heterogeneity of gRNA activity is well recognized, the current understanding of how CRISPR/Cas9 activity is regulated remains incomplete. Here, we identify a sweet spot range of binding free energy change for optimal efficiency which largely explains why gRNAs display changes in efficiency at on- and off-target sites, including why gRNAs can cleave an off-target with higher efficiency than the on-target. Using an energy-based model, we show that local gRNA-DNA interactions resulting from Cas9 "sliding" on overlapping protospacer adjacent motifs (PAMs) profoundly impact gRNA activities. Combining the effects of local sliding for a given PAM context with global off-targets allows us to better identify highly specific, and thus efficient, gRNAs. We validate the effects of local sliding on gRNA efficiency using both public data and in-house data generated by measuring SpCas9 cleavage efficiency at 1024 sites designed to cover all possible combinations of 4-nt PAM and context sequences of 4 gRNAs. Our results provide insights into the mechanisms of Cas9-PAM compatibility and cleavage activation, underlining the importance of accounting for local sliding in gRNA design.

Glucocorticoid receptor triggers a reversible drug-tolerant dormancy state with acquired therapeutic vulnerabilities in lung cancer.

The glucocorticoid receptor (GR) regulates gene expression, governing aspects of homeostasis, but is also involved in cancer. Pharmacological GR activation is frequently used to alleviate therapy-related side-effects. While prior studies have shown GR activation might also have anti-proliferative action on tumours, the underpinnings of glucocorticoid action and its direct effectors in non-lymphoid solid cancers remain elusive. Here, we study the mechanisms of glucocorticoid response, focusing on lung cancer. We show that GR activation induces reversible cancer cell dormancy characterised by anticancer drug tolerance, and activation of growth factor survival signalling accompanied by vulnerability to inhibitors. GR-induced dormancy is dependent on a single GR-target gene, CDKN1C, regulated through chromatin looping of a GR-occupied upstream distal enhancer in a SWI/SNF-dependent fashion. These insights illustrate the importance of GR signalling in non-lymphoid solid cancer biology, particularly in lung cancer, and warrant caution for use of glucocorticoids in treatment of anticancer therapy related side-effects.

Ribo-ODDR: oligo design pipeline for experiment-specific rRNA depletion in Ribo-seq.

MOTIVATION: Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. RESULTS: Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a 'one-size-fits-all' approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. AVAILABILITY AND IMPLEMENTATION: Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BSGatlas: a unified Bacillus subtilis genome and transcriptome annotation atlas with enhanced information access.

A large part of our current understanding of gene regulation in Gram-positive bacteria is based on Bacillus subtilis, as it is one of the most well studied bacterial model systems. The rapid growth in data concerning its molecular and genomic biology is distributed across multiple annotation resources. Consequently, the interpretation of data from further B. subtilis experiments becomes increasingly challenging in both low- and large-scale analyses. Additionally, B. subtilis annotation of structured RNA and non-coding RNA (ncRNA), as well as the operon structure, is still lagging behind the annotation of the coding sequences. To address these challenges, we created the B. subtilis genome atlas, BSGatlas, which integrates and unifies multiple existing annotation resources. Compared to any of the individual resources, the BSGatlas contains twice as many ncRNAs, while improving the positional annotation for 70 % of the ncRNAs. Furthermore, we combined known transcription start and termination sites with lists of known co-transcribed gene sets to create a comprehensive transcript map. The combination with transcription start/termination site annotations resulted in 717 new sets of co-transcribed genes and 5335 untranslated regions (UTRs). In comparison to existing resources, the number of 5' and 3' UTRs increased nearly fivefold, and the number of internal UTRs doubled. The transcript map is organized in 2266 operons, which provides transcriptional annotation for 92 % of all genes in the genome compared to the at most 82 % by previous resources. We predicted an off-target-aware genome-wide library of CRISPR-Cas9 guide RNAs, which we also linked to polycistronic operons. We provide the BSGatlas in multiple forms: as a website (https://rth.dk/resources/bsgatlas/), an annotation hub for display in the UCSC genome browser, supplementary tables and standardized GFF3 format, which can be used in large scale -omics studies. By complementing existing resources, the BSGatlas supports analyses of the B. subtilis genome and its molecular biology with respect to not only non-coding genes but also genome-wide transcriptional relationships of all genes.

Good guide, bad guide: spacer sequence-dependent cleavage efficiency of Cas12a.

Genome editing has recently made a revolutionary development with the introduction of the CRISPR-Cas technology. The programmable CRISPR-associated Cas9 and Cas12a nucleases generate specific dsDNA breaks in the genome, after which host DNA-repair mechanisms can be manipulated to implement the desired editing. Despite this spectacular progress, the efficiency of Cas9/Cas12a-based engineering can still be improved. Here, we address the variation in guide-dependent efficiency of Cas12a, and set out to reveal the molecular basis of this phenomenon. We established a sensitive and robust in vivo targeting assay based on loss of a target plasmid encoding the red fluorescent protein (mRFP). Our results suggest that folding of both the precursor guide (pre-crRNA) and the mature guide (crRNA) have a major influence on Cas12a activity. Especially, base pairing of the direct repeat, other than with itself, was found to be detrimental to the activity of Cas12a. Furthermore, we describe different approaches to minimize base-pairing interactions between the direct repeat and the variable part of the guide. We show that design of the 3' end of the guide, which is not involved in target strand base pairing, may result in substantial improvement of the guide's targeting potential and hence of its genome editing efficiency.

CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters.

BACKGROUND: Recent experimental efforts of CRISPR-Cas9 systems have shown that off-target binding and cleavage are a concern for the system and that this is highly dependent on the selected guide RNA (gRNA) design. Computational predictions of off-targets have been proposed as an attractive and more feasible alternative to tedious experimental efforts. However, accurate scoring of the high number of putative off-targets plays a key role for the success of computational off-targeting assessment. RESULTS: We present an approximate binding energy model for the Cas9-gRNA-DNA complex, which systematically combines the energy parameters obtained for RNA-RNA, DNA-DNA, and RNA-DNA duplexes. Based on this model, two novel off-target assessment methods for gRNA selection in CRISPR-Cas9 applications are introduced: CRISPRoff to assign confidence scores to predicted off-targets and CRISPRspec to measure the specificity of the gRNA. We benchmark the methods against current state-of-the-art methods and show that both are in better agreement with experimental results. Furthermore, we show significant evidence supporting the inverse relationship between the on-target cleavage efficiency and specificity of the system, in which introduced binding energies are key components. CONCLUSIONS: The impact of the binding energies provides a direction for further studies of off-targeting mechanisms. The performance of CRISPRoff and CRISPRspec enables more accurate off-target evaluation for gRNA selections, prior to any CRISPR-Cas9 genome-editing application. For given gRNA sequences or all potential gRNAs in a given target region, CRISPRoff-based off-target predictions and CRISPRspec-based specificity evaluations can be carried out through our webserver at https://rth.dk/resources/crispr/ .

RIsearch2: suffix array-based large-scale prediction of RNA-RNA interactions and siRNA off-targets.

Intermolecular interactions of ncRNAs are at the core of gene regulation events, and identifying the full map of these interactions bears crucial importance for ncRNA functional studies. It is known that RNA-RNA interactions are built up by complementary base pairings between interacting RNAs and high level of complementarity between two RNA sequences is a powerful predictor of such interactions. Here, we present RIsearch2, a large-scale RNA-RNA interaction prediction tool that enables quick localization of potential near-complementary RNA-RNA interactions between given query and target sequences. In contrast to previous heuristics which either search for exact matches while including G-U wobble pairs or employ simplified energy models, we present a novel approach using a single integrated seed-and-extend framework based on suffix arrays. RIsearch2 enables fast discovery of candidate RNA-RNA interactions on genome/transcriptome-wide scale. We furthermore present an siRNA off-target discovery pipeline that not only predicts the off-target transcripts but also computes the off-targeting potential of a given siRNA. This is achieved by combining genome-wide RIsearch2 predictions with target site accessibilities and transcript abundance estimates. We show that this pipeline accurately predicts siRNA off-target interactions and enables off-targeting potential comparisons between different siRNA designs. RIsearch2 and the siRNA off-target discovery pipeline are available as stand-alone software packages from http://rth.dk/resources/risearch.

RAIN: RNA-protein Association and Interaction Networks.

Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded.Database URL: http://rth.dk/resources/rain.

RedNemo: topology-based PPI network reconstruction via repeated diffusion with neighborhood modifications.

Motivation: Analysis of protein-protein interaction (PPI) networks provides invaluable insight into several systems biology problems. High-throughput experimental techniques together with computational methods provide large-scale PPI networks. However, a major issue with these networks is their erroneous nature; they contain false-positive interactions and usually many more false-negatives. Recently, several computational methods have been proposed for network reconstruction based on topology, where given an input PPI network the goal is to reconstruct the network by identifying false-positives/-negatives as correctly as possible. Results: We observe that the existing topology-based network reconstruction algorithms suffer several shortcomings. An important issue is regarding the scalability of their computational requirements, especially in terms of execution times, with the network sizes. They have only been tested on small-scale networks thus far and when applied on large-scale networks of popular PPI databases, the executions require unreasonable amounts of time, or may even crash without producing any output for some instances even after several months of execution. We provide an algorithm, RedNemo, for the topology-based network reconstruction problem. It provides more accurate networks than the alternatives as far as biological qualities measured in terms of most metrics based on gene ontology annotations. The recovery of a high-confidence network modified via random edge removals and rewirings is also better with RedNemo than with the alternatives under most of the experimented removal/rewiring ratios. Furthermore, through extensive tests on databases of varying sizes, we show that RedNemo achieves these results with much better running time performances. Availability and Implementation: Supplementary material including source code, useful scripts, experimental data and the results are available at http://webprs.khas.edu.tr/~cesim/RedNemo.tar.gz. Contact: cesim@khas.edu.tr. Supplementary information: Supplementary data are available at Bioinformatics online.

Joint Profiling of miRNAs and mRNAs Reveals miRNA Mediated Gene Regulation in the Göttingen Minipig Obesity Model.

Obesity and its comorbidities are an increasing challenge for both affected individuals and health care systems, worldwide. In obese individuals, perturbation of expression of both protein-coding genes and microRNAs (miRNA) are seen in obesity-relevant tissues (i.e. adipose tissue, liver and skeletal muscle). miRNAs are small non-coding RNA molecules which have important regulatory roles in a wide range of biological processes, including obesity. Rodents are widely used animal models for human diseases including obesity. However, not all research is applicable for human health or diseases. In contrast, pigs are emerging as an excellent animal model for obesity studies, due to their similarities in their metabolism, their digestive tract and their genetics, when compared to humans. The Göttingen minipig is a small sized easy-to-handle pig breed which has been extensively used for modeling human obesity, due to its capacity to develop severe obesity when fed ad libitum. The aim of this study was to identify differentially expressed of protein-coding genes and miRNAs in a Göttingen minipig obesity model. Liver, skeletal muscle and abdominal adipose tissue were sampled from 7 lean and 7 obese minipigs. Differential gene expression was investigated using high-throughput quantitative real-time PCR (qPCR) on 90 mRNAs and 72 miRNAs. The results revealed de-regulation of several obesity and inflammation-relevant protein-coding genes and miRNAs in all tissues examined. Many genes that are known to be de-regulated in obese humans were confirmed in the obese minipigs and several of these genes have target sites for miRNAs expressed in the opposing direction of the gene, confirming miRNA-mediated regulation in obesity. These results confirm the translational value of the pig for human obesity studies.

SiPAN: simultaneous prediction and alignment of protein-protein interaction networks.

MOTIVATION: Network prediction as applied to protein-protein interaction (PPI) networks has received considerable attention within the last decade. Because of the limitations of experimental techniques for interaction detection and network construction, several computational methods for PPI network reconstruction and growth have been suggested. Such methods usually limit the scope of study to a single network, employing data based on genomic context, structure, domain, sequence information or existing network topology. Incorporating multiple species network data for network reconstruction and growth entails the design of novel models encompassing both network reconstruction and network alignment, since the goal of network alignment is to provide functionally orthologous proteins from multiple networks and such orthology information can be used in guiding interolog transfers. However, such an approach raises the classical chicken or egg problem; alignment methods assume error-free networks, whereas network prediction via orthology works affectively if the functionally orthologous proteins are determined with high precision. Thus to resolve this intertwinement, we propose a framework to handle both problems simultaneously, that of SImultaneous Prediction and Alignment of Networks (SiPAN). RESULTS: We present an algorithm that solves the SiPAN problem in accordance with its simultaneous nature. Bearing the same name as the defined problem itself, the SiPAN algorithm employs state-of-the-art alignment and topology-based interaction confidence construction algorithms, which are used as benchmark methods for comparison purposes as well. To demonstrate the effectiveness of the proposed network reconstruction via SiPAN, we consider two scenarios; one that preserves the network sizes and the other where the network sizes are increased. Through extensive tests on real-world biological data, we show that the network qualities of SiPAN reconstructions are as good as those of original networks and in some cases SiPAN networks are even better, especially for the former scenario. An alternative state-of-the-art network reconstruction algorithm random walk with resistance produces networks considerably worse than the original networks and those reproduced via SiPAN in both cases. AVAILABILITY AND IMPLEMENTATION: Freely available at http://webprs.khas.edu.tr/∼cesim/SiPAN.tar.gz.

BEAMS: backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks.

MOTIVATION: Global many-to-many alignment of biological networks has been a central problem in comparative biological network studies. Given a set of biological interaction networks, the informal goal is to group together related nodes. For the case of protein-protein interaction networks, such groups are expected to form clusters of functionally orthologous proteins. Construction of such clusters for networks from different species may prove useful in determining evolutionary relationships, in predicting the functions of proteins with unknown functions and in verifying those with estimated functions. RESULTS: A central informal objective in constructing clusters of orthologous proteins is to guarantee that each cluster is composed of members with high homological similarity, usually determined via sequence similarities, and that the interactions of the proteins involved in the same cluster are conserved across the input networks. We provide a formal definition of the global many-to-many alignment of multiple protein-protein interaction networks that captures this informal objective. We show the computational intractability of the suggested definition. We provide a heuristic method based on backbone extraction and merge strategy (BEAMS) for the problem. We finally show, through experiments based on biological significance tests, that the proposed BEAMS algorithm performs better than the state-of-the-art approaches. Furthermore, the computational burden of the BEAMS algorithm in terms of execution speed and memory requirements is more reasonable than the competing algorithms. AVAILABILITY AND IMPLEMENTATION: Supplementary material including code implementations in LEDA C++, experimental data and the results are available at http://webprs.khas.edu.tr/~cesim/BEAMS.tar.gz.